• BMB Reports
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• v.39 no.3
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• pp.247-252
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• 2006

A rapid method for the detection of Hepatitis E Virus (HEV) was developed by utilizing nano-gold labeled oligonucleotide probes, silver stain enhancement and the microarray technique. The 5'-end -$NH_2$ modified oligonucleotide probes were immobilized on the surface of the chip base as the capture probe. The detection probe was made of the 3'-end -SH modified oligonucleotide probe and nano-gold colloid. The optimal concentrations of these two probes were determined. To test the detection sensitivity and specificity of this technique, a conservative fragment of the virus RNA was amplified by the RT-PCR/PCR one step amplification. The cDNA was hybridized with the capture probes and the detection probes on microarray. The detection signal was amplified by silver stain enhancement and could be identified by naked eyes. 100 fM of amplicon could be detected out on the microarray. As the results, preparation of nano-gold was improved and faster. Development time also was shortened to 2 min. Thus, considering high efficiency, low cost, good specificity and high sensitivity, this technique is alternative for the detection of HEV.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.3
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• pp.202-208
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• 2011

Recently the technologies that control the computer system through human computer interaction(HCI) have been widely studied. Their applications usually involve the methods that locate user's positions via face detection and recognize user's gestures, but face detection performance is not good enough. In case that the applications do not locate user's position stably, user interface performance, such as gesture recognition, is significantly decreased. In this paper we propose a new stable face detection algorithm using skin color detection and cumulative distribution of face detection results, whose effectiveness was verified by experiments. The propsed algorithm can be applicable in the area of human tracking that uses correspondence matrix analysis.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1559-1565
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• 2007

The many target-detection methods that use forward-looking infrared (FUR) images can deal with large targets measuring $70{\times}40$ pixels, utilizing their shape features. However, detection small targets is difficult because they are more obscure and there are many target-like objects. Therefore, few studies have examined how to detect small targets consisting of fewer than $30{\times}10$ pixels. This paper presents a small target detection method using clutter rejection with stochastic hypothesis testing for FLIR imagery. The proposed algorithm consists of two stages; detection and clutter rejection. In the detection stage, the mean of the input FLIR image is first removed and then the image is segmented using Otsu's method. A closing operation is also applied during the detection stage in order to merge any single targets detected separately. Then, the residual of the clutters is eliminated using statistical hypothesis testing based on the t-test. Several FLIR images are used to prove the performance of the proposed algorithm. The experimental results show that the proposed algorithm accurately detects small targets (Jess than $30{\times}10$ pixels) with a low false alarm rate compared to the center-surround difference method using the receiver operating characteristics (ROC) curve.

• Journal of Korean society of Dental Hygiene
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• v.14 no.5
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• pp.609-615
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• 2014

Objectives : In most developed countries there has been a substantial decrease in the prevalence of dental caries in children of 12 years old over the past 40 years. This change in epidemiology highlights the importance of preventive management in dentistry. For successful management of early caries lesions, it is essential to detect and assess the pathological changes of dental caries. Therefore, the purpose of this study was to investigate the overview of International Caries Detection and Assessment System(ICDAS) criteria for the detection and assessment of early caries lesions by literature reviews. Methods : The author reviewed the MEDLINE search by the key words of 'caries assessment', 'caries detection', 'caries measurement', and 'ICDAS'. Results : This literature review provided an overview of International Caries Detection and Assessment System(ICDAS) criteria for the detection and assessment of early caries lesions. This article showed the principles, concept, and primary caries detection criteria of the ICDAS. The ICDAS can macroscopically measure the enamel surface changes and potential histological depth of caries lesions by the surface characteristics of the teeth. The ICDAS criteria allow researchers and clinicians to choose the stage of dental caries and characteristics for assessment. Conclusions : ICDAS can be an adjunct method to clinical decision making and preventive treatment planning.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.137-144
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• 2017

Face detection is still a challenging task under severe face pose variations in complex background. This paper proposes an effective algorithm which can detect single or multiple faces based on skin color detection and depth information. We introduce Gaussian mixture model(GMM) for skin color detection in a color image. The depth information is from three dimensional depth sensor of Kinect V2 device, and is useful in segmenting a human body from the background. Then, a labeling process successfully removes non-face region using several features. Experimental results show that the proposed face detection algorithm can provide robust detection performance even under variable conditions and complex background.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.9-18
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• 2017

Ubiquitous computing is a novel paradigm that is rapidly gaining in the scenario of wireless communications and telecommunications for realizing smart world. As rapid development of sensor technology, smart sensor system becomes more popular in automobile or vehicle. In this study, a new vehicle detection mechanism in real-time for blind spot area is proposed based on imaging sensors. To determine the position of other vehicles on the road is important for operation of driver assistance systems (DASs) to increase driving safety. As the result, blind spot detection of vehicles is addressed using an automobile detection algorithm for blind spots. The proposed vehicle verification utilizes the height and angle of a rear-looking vehicle mounted camera. Candidate vehicle information is extracted using adaptive shadow detection based on brightness values of an image of a vehicle area. The vehicle is verified using a training set with Haar-like features of candidate vehicles. Using these processes, moving vehicles can be detected in blind spots. The detection ratio of true vehicles was 91.1% in blind spots based on various experimental results.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.23-29
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• 2009

Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real-time multi-channel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in a embedded system using them turns out prohibitively difficult. In this paper, we propose a fast and precise blob detection algorithm for visual surveillance. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need much different processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that the developed foreground mask correction method can be incorporated into connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.144-147
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• 2004

Two different sensors (here, KOMPSAT and RADARSAT) are considered for ship detection, and are used to delineate the detection performance for their data. The experiments are set for coastal regions of Mokpo Port and Ulsan Port and field experiments on board pilot boat are conducted to collect in situ ship validation information such as ship type and length. This paper introduce mainly the experiment result of ship detection by both RADARSAT SAR imagery and landbased RADAR data, operated by the local Authority of South Korea, so called vessel traffic system (VTS) radar. Fine imagery of Ulsan Port was acquired on June 19, 2004 and in-situ data such as wind speed and direction, taking pictures of ships and natural features were obtained aboard a pilot ship. North winds, with a maximum speed of 3.1 m/s were recorded. Ship's position, size and shape and natural features of breakwaters, oil pipeline and alongside ship were compared using SAR and VTS. It is shown that KOMPSAT/EOC has a good performance in the detection of a moving ship at a speed of 7 kts or more an hour that ship and its wake can be imaged. The detection capability of RADARSAT doesn't matter how fast ship is running and depends on a ship itself, e.g. its material, length and type. Our results indicate that SAR can be applicable to automated ship detection for a VTS and SAR combination service.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.677-679
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• 1999

This paper introduces a implementation of fault detection for national highway line 3. Fault detection system was installed and operated on national highway line 3, environmental elements caused by abnormal status or faults has often happened. Therefore, the function of fault detection system is to speedy notify fault site, cause as well as scale of fault to manager. Though the fault detection and diagnosis system has been imported in the field of process of water and electric power, it is just beginning step in the field of ITS(Intelligent Transportation Systems). In general, Maintenance system is performed the online/offline process of detection, diagnosis and measure. This paper is studied online detection process, which is realtime remote detection.